Lightweight Anti-UAV Object Tracking With Visual Sensing Based on Heterogeneous Model Knowledge Distillation

The widespread application of Unmanned Aerial Vehicles (UAVs) presents substantial challenges to public safety, thereby necessitating accurate visual tracking for effective counter-UAV operations. However, most existing transformer-based trackers rely on increasingly large and deep networks to improve accuracy in complex backgrounds, which constrains their practicality in real-world deployments. To address this limitation, we propose a lightweight anti‑UAV tracker based on knowledge distillation. Specifically, we design a fully transformer-based anti‑UAV teacher network (TransADT) and develop a lightweight Siamese student network (SiamGLT) constructed with on a gated linear transformer, enabling accurate real-time tracking. In addition, we introduce a heterogeneous model knowledge distillation framework (HKD) to facilitate knowledge transfer from the large transformer teacher to the convolutional student network. This framework comprises three types of knowledge transfer: foreground and background feature decoupled distillation, instance-wise relation distillation, and response knowledge distillation. Comprehensive experiments on Anti-UAV benchmarks and real-world scenarios demonstrate that the proposed tracker surpasses other state-of-the-art methods. Our SiamGLT model achieves an optimal balance between tracking accuracy and computational efficiency.